GCN Context: \(\mathbf {c}_i\)  is generated from a Graph Convolutional Network (GCN)  [1]}, [2]}, [3]}.
Given a graph with \(n\)  nodes, the input to a GCN is a feature matrix \(\mathbf {X} \in \mathbb {R}^{n \times m}\)  where a row contains the initial representation, \(x_{u} \in \mathbb {R}^{m}\)  of a node \(u\) . To compute a node's hidden representation, \(h_u \in \mathbb {R}^{d}\) , a single graph convolution layer uses the node's neighbors \(\mathbf {N}(u)\) ,:
\(h_{u} = f\left(\sum _{v \in \mathbf {N}(u)}\left(\mathbf {w} x_{v} + b\right)\right) \) 
